Authors : Dr. Kavitha K S; Mamatha C G

Volume/Issue : Volume 9 - 2024, Issue 6 - June

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Google Scholar : https://tinyurl.com/mr384ktj

Scribd : https://tinyurl.com/65r6mrr9

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUN794

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : Ensuring product quality and minimizing defects is crucial in today's manufacturing industry. Traditional manual inspections are labor-intensive and error prone.This paper describes a system designed to identify defects automatically the YOLOv5 algorithm, known for its accuracy and speed. High-resolution images of products are processed with YOLOv5 to identify defects like scratches, dents, and deformations. This system enhances sorting and quality assurance, improving efficiency and consistency. Experimental results show YOLOv5 superior performance in detection accuracy and speed compared to traditional methods, exploring the feasibility of combining machine learning and image processing within manufacturing.

Keywords : Automated Defect Detection, Image Processing, Yolov5, Quality Assurance, Manufacturing, Object Detection, Machine Learning.

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